Automated parameter optimization should be included in future defect prediction studies
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Automated parameter optimization should be included in future defect prediction studies
- 1. Automated Parameter Optimization for Defect Prediction Models Chakkrit (Kla) Tantithamthavorn Shane McIntosh Ahmed E. Hassan Kenichi Matsumoto http://chakkrit.com kla@chakkrit.com @klainfo
- 2. Defect models are used to predict software modules that are likely to be defective in the future 2 Pre-release period Releasedate Post-release period Defect prediction models Module A Module C Module B Module D Module A Module C Module B Module D Clean Defect-prone Clean Defect-prone
- 3. Defect models are trained using classification techniques 3 1 2 Decision Tree Algorithms Regression Algorithms Clustering Algorithms Ensemble Algorithms
- 4. Such classification techniques often require parameter settings 4 Ensemble Algorithms
- 5. Such classification techniques often require parameter settings 4 The number of trees in a random forest classifier Ensemble Algorithms
- 6. Such classification techniques often require parameter settings 4 The number of trees in a random forest classifier 26 of the 30 most commonly used classification techniques require at least one parameter setting Ensemble Algorithms
- 7. 5 Defect models may underperform if they are trained using suboptimal parameter settings The default settings of random forest, naïve bayes, and support vector machines are suboptimal [Jiang et al., DEFECTS’08] [Tosun et al., ESEM’09] [Hall et al., TSE’12]
- 8. 6 Different toolkits have different default settings for the same classification technique randomForest package Default setting of the number of trees in a random forest 10 50 100 500 bigrf package
- 9. 7 The parameter space is too large for manual inspection There are at least 17,000 possible settings to explore when training k-NN classifiers [Kocaguneli et al., TSE’12]
- 10. 8 The parameter space is too large for manual inspection There are at least 17,000 possible settings to explore when training k-NN classifiers [Kocaguneli et al., TSE’12] How do automated parameter optimization techniques fare when applied to defect prediction?
- 14. Caret — an off-the-shelf automated parameter optimization technique 12 (Step-1) Generate candidate settings Settings (Step-2) Evaluate candidate settings Performance for each setting (Step-3) Identify optimal setting Optimal setting
- 15. Generate a set of candidate settings to evaluate 13 #Trees for random forest #Trees = 10 #Trees = 20 #Trees = 30 #Trees = 40 #Trees = 50 (Step-1) Generate candidate settings
- 16. Evaluate the performance of each candidate setting using bootstrap validation 14 Defect Dataset Testing Corpus Training Corpus Generate bootstrap samples Construct defect model Model Calculate performance Perf. Out-of-sample Bootstrap Validation with 100 repetitions (Step-1) Generate candidate settings (Step-2) Evaluate candidate settings
- 17. The optimal setting is the one that achieved the top performance score 15 AUC=0.65 AUC=0.68 AUC=0.70 AUC=0.80 AUC=0.86 10 20 30 40 50 (Step-1) Generate candidate settings (Step-2) Evaluate candidate settings (Step-3) Identify optimal setting
- 18. We study a collection of 18 datasets from 5 open corpora 16 A threat of bias exists if researchers fixate on studying the same datasets with the same metrics [Tantithamthavorn et al., TSE’16]
- 19. We study a collection of 18 datasets from 5 open corpora 16 1-7K Modules 21-28% Defective Rate 21-38 Metrics [Shepperd et al., TSE’13] 1-10K Modules 11-44% Defective Rate 15-32 Metrics [Zimmermann et al., PROMISE’07] [D’Ambros et al., MSR’10] [Kim et al., ICSE’11] 600-800 Modules 36-48% Defective Rate 20 Metrics [Jureczko et al., PROMISE’10] A threat of bias exists if researchers fixate on studying the same datasets with the same metrics [Tantithamthavorn et al., TSE’16]
- 20. Compute the performance improvement 17 Construct + evaluate Caret-optimized models Optimized setting Defect dataset Construct + evaluate default models Default setting
- 21. Compute the performance improvement 17 Construct + evaluate Caret-optimized models Optimized setting Defect dataset Construct + evaluate default models Default setting Caret-optimized performance 100x Technique 1 AUC default performance 100x Technique 1 AUC
- 22. Compute the performance improvement 17 Construct + evaluate Caret-optimized models Optimized setting Defect dataset Construct + evaluate default models Default setting Caret-optimized performance 100x Technique 1 AUC default performance 100x Technique 1 AUC Average Average
- 23. Compute the performance improvement 17 Construct + evaluate Caret-optimized models Optimized setting Defect dataset Construct + evaluate default models Default setting Caret-optimized performance 100x Technique 1 AUC default performance 100x Technique 1 AUC Average Average Performance Improvement
- 24. Large Medium Small ● ● ● ● ● ● ● ● 0.0 0.1 0.2 0.3 0.4 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adia G AM AUCPerformanceImprovement Parameter settings can substantially influence the performance of defect prediction models 18 Each boxplot presents the performance improvement for all the 18 studied datasets
- 25. Large Medium Small ● ● ● ● ● ● ● ● 0.0 0.1 0.2 0.3 0.4 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adia G AM AUCPerformanceImprovement Parameter settings can substantially influence the performance of defect prediction models 19 9 of the 26 studied classification techniques have a large performance improvement
- 26. Large Medium Small ● ● ● ● ● ● ● ● 0.0 0.1 0.2 0.3 0.4 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adia G AM AUCPerformanceImprovement Parameter settings can substantially influence the performance of defect prediction models 20 C5.0 and AdaBoost have a median improvement of 0.27 and 0.14 AUC
- 27. Large Medium Small ● ● ● ● ● ● ● ● 0.0 0.1 0.2 0.3 0.4 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adia G AM AUCPerformanceImprovement Parameter settings can substantially influence the performance of defect prediction models 21 C5.0 and AdaBoost span up to 0.40 AUC
- 28. Large Medium Small Ne ● ● ● ● ● ● ● ● ● ● ● 0.0 0.1 0.2 0.3 0.4 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adial G AM Boost R FR ippe AUCPerformanceImprovement 22 Caret improves the AUC performance by up to 40 percentage points Performance Improvement Performance Stability
- 29. 23 Performance Stability Large Medium Small Ne ● ● ● ● ● ● ● ● ● ● ● 0.0 0.1 0.2 0.3 0.4 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adial G AM Boost R FR ippe AUCPerformanceImprovement Caret improves the AUC performance by up to 40 percentage points Performance Improvement
- 30. 24 Default settings may introduce instability into defect prediction models Unstable performance estimates may introduce bias into the conclusion of research [Jorgensen et al., TSE’07] [Menzies and Shepperd, EMSE’12]
- 31. Estimating the stability of defect prediction models 25 Construct + evaluate Caret-optimized models Optimized setting Construct + evaluate default models Default setting Defect dataset
- 32. Estimating the stability of defect prediction models 25 Construct + evaluate Caret-optimized models Optimized setting Construct + evaluate default models Default setting Caret-optimized performance 100x Technique 1 AUC Default performance 100x Technique 1 AUC Defect dataset
- 33. Estimating the stability of defect prediction models 25 Construct + evaluate Caret-optimized models Optimized setting Construct + evaluate default models Default setting Caret-optimized performance 100x Technique 1 AUC Default performance 100x Technique 1 AUC Defect dataset Standard Deviation (S.D.) Standard Deviation (S.D.)
- 34. Estimating the stability of defect prediction models 25 Construct + evaluate Caret-optimized models Optimized setting Construct + evaluate default models Default setting Caret-optimized performance 100x Technique 1 AUC Default performance 100x Technique 1 AUC Stability Ratio = S.D. of Optimized S.D. of Default Defect dataset Standard Deviation (S.D.) Standard Deviation (S.D.)
- 35. Large Medium Small 0.0 0.5 1.0 1.5 2.0 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R ad G A StabilityRatio Caret-optimized classifiers tend to be more stable than default classifiers 26 Cliff’s delta = Large Each boxplot presents the stability ratio for all the 18 studied datasets
- 36. Large Medium Small 0.0 0.5 1.0 1.5 2.0 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R ad G A StabilityRatio Caret-optimized classifiers tend to be more stable than default classifiers 27 Cliff’s delta = Large A stability ratio lower than one indicates that Caret-optimized classifiers tend to be more stable than default classifiers
- 37. Large Medium Small 0.0 0.5 1.0 1.5 2.0 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R ad G A StabilityRatio Caret-optimized classifiers tend to be more stable than default classifiers 28 Stability ratio is lower than 1 for 35% of the studied classification techniques Cliff’s delta = Large
- 38. Medium Small Negligible D ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adial G AM Boost R FR ipperPM R PDAM AR S SVM Linear J48 Caret-optimized classifiers are at least as stable as default classifiers 29 Stability ratio is about 0 for 65% of the studied classification techniques
- 39. Large 0.0 0.5 1.0 1.5 2.0 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP L StabilityRatio 30 Performance Stability Large Medium Small Ne ● ● ● ● ● ● ● ● ● ● ● 0.0 0.1 0.2 0.3 0.4 C 5.0 AdaBoost AVN N etC ART PC AN N etN N etFDA M LPW eightD ecayM LP LM TG PLS LogitBoostKN N xG BTreeG BM N B R BF SVM R adial G AM Boost R FR ippe AUCPerformanceImprovement Performance Improvement Caret improves the AUC performance by up to 40 percentage points. Caret-optimized classifiers tend to be more stable than classifiers trained with default settings
- 40. 31 Suboptimal parameter settings may have an impact on prior defect prediction results!
- 41. Prior findings on top- performing classification techniques 32 17 of 22 classification techniques are indistinguishable [Lessmann et. al. TSE’08] Classification techniques have a large impact on the performance [Ghotra et al., ICSE’15]
- 42. Prior findings on top- performing classification techniques 32 However, these studies have not taken parameter optimization into account 17 of 22 classification techniques are indistinguishable [Lessmann et. al. TSE’08] Classification techniques have a large impact on the performance [Ghotra et al., ICSE’15]
- 43. Identifying statistically distinct ranks of classification techniques 33 100x Technique 1 AUC Performance Distribution 100x Technique 26 AUC Performance Distribution Dataset 1 ….100x Technique 2 AUC Performance Distribution
- 44. Identifying statistically distinct ranks of classification techniques 33 Scott-Knott ESD test 100x Technique 1 AUC Performance Distribution 100x Technique 26 AUC Performance Distribution Dataset 1 ….100x Technique 2 AUC Performance Distribution
- 45. Identifying statistically distinct ranks of classification techniques 33 Scott-Knott ESD test 100x Technique 1 AUC Performance Distribution 100x Technique 26 AUC Performance Distribution Dataset 1 ….100x Technique 2 AUC Performance Distribution Ranking for dataset 1
- 46. Identifying statistically distinct ranks of classification techniques 33 Scott-Knott ESD test 100x Technique 1 AUC Performance Distribution 100x Technique 26 AUC Performance Distribution Dataset 1 ….100x Technique 2 AUC Performance Distribution Ranking for dataset 1 Ranking for Dataset 18 Scott-Knott ESD test Dataset 18 100x Technique 1 AUC Performance Distribution 100x Technique 26 AUC Performance Distribution ….100x Technique 2 AUC Performance Distribution
- 47. Identifying statistically distinct ranks of classification techniques 34 Pool of ranking for each dataset Dataset T1 T2 T3 1 2 1 3 2 1 2 3 3 1 1 2 Scott-Knott ESD test 100x Technique 26 AUC Performance Distribution Dataset 1 Ranking for dataset 1 Ranking for Dataset 18 Scott-Knott ESD test Dataset 18 100x Technique 26 AUC Performance Distribution
- 48. Pool of ranking for each dataset Dataset T1 T2 T3 1 2 1 3 2 1 2 3 3 1 1 2 Compute the proportion of datasets where a classifier appears in the top rank 35 Likelihood for each technique T1 T2 T3 0.67 0.67 0 Compute likelihood
- 49. Pool of ranking for each dataset Dataset T1 T2 T3 1 2 1 3 2 1 2 3 3 1 1 2 Compute the proportion of datasets where a classifier appears in the top rank 36 Likelihood for each technique T1 T2 T3 0.67 0.67 0 Compute likelihood
- 50. Bootstrap resampling to combat sample selection bias 37 Bootstrap sample of ranking Bootstrap Sampling Pool of ranking for each dataset Dataset T1 T2 T3 1 2 1 3 2 1 2 3 3 1 1 2 Dataset T1 T2 T3 1 2 1 3 2 1 2 3 2 1 2 3
- 51. Re-compute the likelihood for each sample 38 Likelihood for each technique T1 T2 T3 0.67 0.33 0 Bootstrap Sampling Pool of ranking for each dataset Dataset T1 T2 T3 1 2 1 3 2 1 2 3 3 1 1 2 Dataset T1 T2 T3 1 2 1 3 2 1 2 3 2 1 2 3 Compute likelihood Bootstrap sample of ranking
- 52. Repeat the bootstrap 100 times to estimate the confidence interval 39 Bootstrap Sampling Pool of ranking for each dataset Dataset T1 T2 T3 1 2 1 3 2 1 2 3 3 1 1 2 Dataset T1 T2 T3 1 2 1 3 2 1 2 3 2 1 2 3 Bootstrap sample of ranking
- 53. Repeat the bootstrap 100 times to estimate the confidence interval 39 Bootstrap Sampling Pool of ranking for each dataset Dataset T1 T2 T3 1 2 1 3 2 1 2 3 3 1 1 2 Dataset T1 T2 T3 1 2 1 3 2 1 2 3 2 1 2 3 Repeat 100 times T1 T2 T3 0.67 0.33 0 … … … 0.33 0 0 Distribution of likelihood Compute likelihood Bootstrap sample of ranking
- 54. Caret optimization can substantially shift the top-ranked classification techniques 40 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PLS PDA N N et PM R Boost N N et AR S FDA Boost adial ecay M LP R BF N B ipper LM T ●Optimized Classifier Default Classifier ● ● ● ● ● ● ● ● ● ● ● ● ● ●0.0 0.2 0.4 0.6 0.8 1.0 C 5.0xG BTreeAVN N et G BM R FG PLS PDA N N et PM RAM Boost PC AN N etM AR S FDA AdaBoost VM R adia igh Likelihood ●Optimized Classifier D Top-ranklikelihoodestimate
- 55. Caret optimization can substantially shift the top-ranked classification techniques 41 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PLS PDA N N et PM R Boost N N et AR S FDA Boost adial ecay M LP R BF N B ipper LM T ●Optimized Classifier Default Classifier ● ● ● ● ● ● ● ● ● ● ● ● ● ●0.0 0.2 0.4 0.6 0.8 1.0 C 5.0xG BTreeAVN N et G BM R FG PLS PDA N N et PM RAM Boost PC AN N etM AR S FDA AdaBoost VM R adia igh Likelihood ●Optimized Classifier D Top-ranklikelihoodestimate
- 56. Caret optimization can substantially shift the top-ranked classification techniques 42 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PLS PDA N N et PM R Boost N N et AR S FDA Boost adial ecay M LP R BF N B ipper LM T ●Optimized Classifier Default Classifier ● ● ● ● ● ● ● ● ● ● ● ● ● ●0.0 0.2 0.4 0.6 0.8 1.0 C 5.0xG BTreeAVN N et G BM R FG PLS PDA N N et PM RAM Boost PC AN N etM AR S FDA AdaBoost VM R adia igh Likelihood ●Optimized Classifier D Top-ranklikelihoodestimate Caret increases the likelihood of appearing in the top rank by up to 83%
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